{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7fb4d674",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "626f29a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "today_world = pd.read_csv(\"./today_world_2021_05_27.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "642ea013",
   "metadata": {},
   "outputs": [
    {
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       "      <td>澳大利亚</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6</td>\n",
       "      <td>德国</td>\n",
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       "    <tr>\n",
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       "      <td>7</td>\n",
       "      <td>美国</td>\n",
       "      <td>2021-05-27 10:02:47</td>\n",
       "      <td>33971207</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "        id  name       lastUpdateTime  total_confirm  total_suspect  \\\n",
       "0  9577772   突尼斯  2021-05-27 08:27:36         338853              0   \n",
       "1  9507896  塞尔维亚  2021-05-27 08:27:36         711116              0   \n",
       "2        0    中国  2021-05-27 08:27:41         109016              0   \n",
       "3        1  日本本土  2021-05-27 14:07:43         731073              0   \n",
       "4        2    泰国  2021-05-27 08:27:36         137894              0   \n",
       "5        3   新加坡  2021-05-27 08:27:36          61916              0   \n",
       "6        4    韩国  2021-05-27 10:57:32         138311              0   \n",
       "7        5  澳大利亚  2021-05-27 09:57:30          30063              0   \n",
       "8        6    德国  2021-05-27 13:02:36        3667041              0   \n",
       "9        7    美国  2021-05-27 10:02:47       33971207              0   \n",
       "\n",
       "   total_heal  total_dead  total_severe  total_input  today_confirm  \\\n",
       "0      298855       12398             0          0.0         1324.0   \n",
       "1           0        6811             0          0.0          387.0   \n",
       "2       98818        4892             0       6012.0          655.0   \n",
       "3      649955       12573             0          0.0            2.0   \n",
       "4       26873         873             0          0.0         2455.0   \n",
       "5       61360          32             0          0.0           26.0   \n",
       "6      128180        1943             0          0.0          629.0   \n",
       "7       23568         910             0          0.0           15.0   \n",
       "8     3452290       88000             0          0.0         1846.0   \n",
       "9    27662268      606179             0          0.0        23500.0   \n",
       "\n",
       "   today_suspect  today_heal  today_dead  today_severe  today_storeConfirm  \\\n",
       "0            NaN       714.0        56.0           NaN                 NaN   \n",
       "1            NaN         0.0         0.0           NaN                 NaN   \n",
       "2            NaN        13.0        11.0           NaN               631.0   \n",
       "3            NaN         0.0         1.0           NaN                 NaN   \n",
       "4            NaN         0.0         0.0           NaN                 NaN   \n",
       "5            NaN        31.0         0.0           NaN                 NaN   \n",
       "6            NaN       598.0         3.0           NaN                 NaN   \n",
       "7            NaN         6.0         0.0           NaN                 NaN   \n",
       "8            NaN     12720.0       227.0           NaN                 NaN   \n",
       "9            NaN     55826.0       971.0           NaN                 NaN   \n",
       "\n",
       "   today_input  \n",
       "0          NaN  \n",
       "1          NaN  \n",
       "2         17.0  \n",
       "3          NaN  \n",
       "4          NaN  \n",
       "5          NaN  \n",
       "6          NaN  \n",
       "7          NaN  \n",
       "8          NaN  \n",
       "9          NaN  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "today_world.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6c8741c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "dict1 = {\"name\":\"名称\"}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "88f02bc7",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "        id    名称       lastUpdateTime  total_confirm  total_suspect  \\\n",
       "0  9577772   突尼斯  2021-05-27 08:27:36         338853              0   \n",
       "1  9507896  塞尔维亚  2021-05-27 08:27:36         711116              0   \n",
       "2        0    中国  2021-05-27 08:27:41         109016              0   \n",
       "3        1  日本本土  2021-05-27 14:07:43         731073              0   \n",
       "4        2    泰国  2021-05-27 08:27:36         137894              0   \n",
       "\n",
       "   total_heal  total_dead  total_severe  total_input  today_confirm  \\\n",
       "0      298855       12398             0          0.0         1324.0   \n",
       "1           0        6811             0          0.0          387.0   \n",
       "2       98818        4892             0       6012.0          655.0   \n",
       "3      649955       12573             0          0.0            2.0   \n",
       "4       26873         873             0          0.0         2455.0   \n",
       "\n",
       "   today_suspect  today_heal  today_dead  today_severe  today_storeConfirm  \\\n",
       "0            NaN       714.0        56.0           NaN                 NaN   \n",
       "1            NaN         0.0         0.0           NaN                 NaN   \n",
       "2            NaN        13.0        11.0           NaN               631.0   \n",
       "3            NaN         0.0         1.0           NaN                 NaN   \n",
       "4            NaN         0.0         0.0           NaN                 NaN   \n",
       "\n",
       "   today_input  \n",
       "0          NaN  \n",
       "1          NaN  \n",
       "2         17.0  \n",
       "3          NaN  \n",
       "4          NaN  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "today_world.rename(columns=dict1).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4e64e3ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "name_dict = {'date':'日期','name':'名称','id':'编号','lastUpdateTime':'更新时间',\n",
    "             'today_confirm':'当日新增确诊','today_suspect':'当日新增疑似',\n",
    "             'today_heal':'当日新增治愈','today_dead':'当日新增死亡',\n",
    "             'today_severe':'当日新增重症','today_storeConfirm':'当日现存确诊',\n",
    "             'today_input':'当日境外输入',\n",
    "             'total_confirm':'累计确诊','total_suspect':'累计疑似',\n",
    "             'total_heal':'累计治愈','total_dead':'累计死亡','total_severe':'累计重症',\n",
    "             'total_input':'累计境外输入'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ea63a650",
   "metadata": {},
   "outputs": [],
   "source": [
    "today_world.rename(columns=name_dict,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2bdc72b2",
   "metadata": {},
   "outputs": [
    {
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       "      <th>累计疑似</th>\n",
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       "      <th>当日新增重症</th>\n",
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       "        编号    名称                 更新时间    累计确诊  累计疑似    累计治愈   累计死亡  累计重症  \\\n",
       "0  9577772   突尼斯  2021-05-27 08:27:36  338853     0  298855  12398     0   \n",
       "1  9507896  塞尔维亚  2021-05-27 08:27:36  711116     0       0   6811     0   \n",
       "2        0    中国  2021-05-27 08:27:41  109016     0   98818   4892     0   \n",
       "3        1  日本本土  2021-05-27 14:07:43  731073     0  649955  12573     0   \n",
       "4        2    泰国  2021-05-27 08:27:36  137894     0   26873    873     0   \n",
       "\n",
       "   累计境外输入  当日新增确诊  当日新增疑似  当日新增治愈  当日新增死亡  当日新增重症  当日现存确诊  当日境外输入  \n",
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       "4     0.0  2455.0     NaN     0.0     0.0     NaN     NaN     NaN  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "today_world.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dd61ddf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "today_world['当日现存确诊'] = today_world['累计确诊']-today_world['累计治愈']-today_world['累计死亡']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "79cef117",
   "metadata": {},
   "outputs": [
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       "        编号    名称                 更新时间    累计确诊  累计疑似    累计治愈   累计死亡  累计重症  \\\n",
       "0  9577772   突尼斯  2021-05-27 08:27:36  338853     0  298855  12398     0   \n",
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       "2        0    中国  2021-05-27 08:27:41  109016     0   98818   4892     0   \n",
       "3        1  日本本土  2021-05-27 14:07:43  731073     0  649955  12573     0   \n",
       "4        2    泰国  2021-05-27 08:27:36  137894     0   26873    873     0   \n",
       "\n",
       "   累计境外输入  当日新增确诊  当日新增疑似  当日新增治愈  当日新增死亡  当日新增重症  当日现存确诊  当日境外输入  \n",
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    "today_world.head()"
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  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dad7a7a5",
   "metadata": {},
   "outputs": [
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     "output_type": "stream",
     "text": [
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      " 4   累计疑似    207 non-null    int64  \n",
      " 5   累计治愈    207 non-null    int64  \n",
      " 6   累计死亡    207 non-null    int64  \n",
      " 7   累计重症    207 non-null    int64  \n",
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      " 12  当日新增死亡  160 non-null    float64\n",
      " 13  当日新增重症  13 non-null     float64\n",
      " 14  当日现存确诊  207 non-null    int64  \n",
      " 15  当日境外输入  6 non-null      float64\n",
      "dtypes: float64(7), int64(6), object(3)\n",
      "memory usage: 26.0+ KB\n"
     ]
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   "source": [
    "today_world.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2089b619",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "49e7495f",
   "metadata": {},
   "outputs": [],
   "source": [
    "today_world['病死率'] = (today_world['累计死亡']/today_world['累计确诊']).apply(lambda x: format(x, '.2f')) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7a32ad34",
   "metadata": {},
   "outputs": [
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       "      <td>17.0</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>日本本土</td>\n",
       "      <td>2021-05-27 14:07:43</td>\n",
       "      <td>731073</td>\n",
       "      <td>0</td>\n",
       "      <td>649955</td>\n",
       "      <td>12573</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68545</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>泰国</td>\n",
       "      <td>2021-05-27 08:27:36</td>\n",
       "      <td>137894</td>\n",
       "      <td>0</td>\n",
       "      <td>26873</td>\n",
       "      <td>873</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2455.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>110148</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        编号    名称                 更新时间    累计确诊  累计疑似    累计治愈   累计死亡  累计重症  \\\n",
       "0  9577772   突尼斯  2021-05-27 08:27:36  338853     0  298855  12398     0   \n",
       "1  9507896  塞尔维亚  2021-05-27 08:27:36  711116     0       0   6811     0   \n",
       "2        0    中国  2021-05-27 08:27:41  109016     0   98818   4892     0   \n",
       "3        1  日本本土  2021-05-27 14:07:43  731073     0  649955  12573     0   \n",
       "4        2    泰国  2021-05-27 08:27:36  137894     0   26873    873     0   \n",
       "\n",
       "   累计境外输入  当日新增确诊  当日新增疑似  当日新增治愈  当日新增死亡  当日新增重症  当日现存确诊  当日境外输入   病死率  \n",
       "0     0.0  1324.0     NaN   714.0    56.0     NaN   27600     NaN  0.04  \n",
       "1     0.0   387.0     NaN     0.0     0.0     NaN  704305     NaN  0.01  \n",
       "2  6012.0   655.0     NaN    13.0    11.0     NaN    5306    17.0  0.04  \n",
       "3     0.0     2.0     NaN     0.0     1.0     NaN   68545     NaN  0.02  \n",
       "4     0.0  2455.0     NaN     0.0     0.0     NaN  110148     NaN  0.01  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "today_world.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "adebc5af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 207 entries, 0 to 206\n",
      "Data columns (total 17 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   编号      207 non-null    object \n",
      " 1   名称      207 non-null    object \n",
      " 2   更新时间    207 non-null    object \n",
      " 3   累计确诊    207 non-null    int64  \n",
      " 4   累计疑似    207 non-null    int64  \n",
      " 5   累计治愈    207 non-null    int64  \n",
      " 6   累计死亡    207 non-null    int64  \n",
      " 7   累计重症    207 non-null    int64  \n",
      " 8   累计境外输入  170 non-null    float64\n",
      " 9   当日新增确诊  160 non-null    float64\n",
      " 10  当日新增疑似  13 non-null     float64\n",
      " 11  当日新增治愈  160 non-null    float64\n",
      " 12  当日新增死亡  160 non-null    float64\n",
      " 13  当日新增重症  13 non-null     float64\n",
      " 14  当日现存确诊  207 non-null    int64  \n",
      " 15  当日境外输入  6 non-null      float64\n",
      " 16  病死率     207 non-null    object \n",
      "dtypes: float64(7), int64(6), object(4)\n",
      "memory usage: 27.6+ KB\n"
     ]
    }
   ],
   "source": [
    "today_world.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0c163908",
   "metadata": {},
   "outputs": [],
   "source": [
    "today_world['病死率'] = today_world['病死率'].astype(\"float\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "68f1367e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 207 entries, 0 to 206\n",
      "Data columns (total 17 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   编号      207 non-null    object \n",
      " 1   名称      207 non-null    object \n",
      " 2   更新时间    207 non-null    object \n",
      " 3   累计确诊    207 non-null    int64  \n",
      " 4   累计疑似    207 non-null    int64  \n",
      " 5   累计治愈    207 non-null    int64  \n",
      " 6   累计死亡    207 non-null    int64  \n",
      " 7   累计重症    207 non-null    int64  \n",
      " 8   累计境外输入  170 non-null    float64\n",
      " 9   当日新增确诊  160 non-null    float64\n",
      " 10  当日新增疑似  13 non-null     float64\n",
      " 11  当日新增治愈  160 non-null    float64\n",
      " 12  当日新增死亡  160 non-null    float64\n",
      " 13  当日新增重症  13 non-null     float64\n",
      " 14  当日现存确诊  207 non-null    int64  \n",
      " 15  当日境外输入  6 non-null      float64\n",
      " 16  病死率     207 non-null    float64\n",
      "dtypes: float64(8), int64(6), object(3)\n",
      "memory usage: 27.6+ KB\n"
     ]
    }
   ],
   "source": [
    "today_world.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3d36befd",
   "metadata": {},
   "outputs": [],
   "source": [
    "today_world.set_index('名称',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "679e7fbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <th>突尼斯</th>\n",
       "      <td>9577772</td>\n",
       "      <td>2021-05-27 08:27:36</td>\n",
       "      <td>338853</td>\n",
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       "      <td>56.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27600</td>\n",
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       "      <th>塞尔维亚</th>\n",
       "      <td>9507896</td>\n",
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       "      <td>711116</td>\n",
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       "    <tr>\n",
       "      <th>中国</th>\n",
       "      <td>0</td>\n",
       "      <td>2021-05-27 08:27:41</td>\n",
       "      <td>109016</td>\n",
       "      <td>0</td>\n",
       "      <td>98818</td>\n",
       "      <td>4892</td>\n",
       "      <td>0</td>\n",
       "      <td>6012.0</td>\n",
       "      <td>655.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5306</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日本本土</th>\n",
       "      <td>1</td>\n",
       "      <td>2021-05-27 14:07:43</td>\n",
       "      <td>731073</td>\n",
       "      <td>0</td>\n",
       "      <td>649955</td>\n",
       "      <td>12573</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.02</td>\n",
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       "    <tr>\n",
       "      <th>泰国</th>\n",
       "      <td>2</td>\n",
       "      <td>2021-05-27 08:27:36</td>\n",
       "      <td>137894</td>\n",
       "      <td>0</td>\n",
       "      <td>26873</td>\n",
       "      <td>873</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2455.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>110148</td>\n",
       "      <td>NaN</td>\n",
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       "           编号                 更新时间    累计确诊  累计疑似    累计治愈   累计死亡  累计重症  累计境外输入  \\\n",
       "名称                                                                              \n",
       "突尼斯   9577772  2021-05-27 08:27:36  338853     0  298855  12398     0     0.0   \n",
       "塞尔维亚  9507896  2021-05-27 08:27:36  711116     0       0   6811     0     0.0   \n",
       "中国          0  2021-05-27 08:27:41  109016     0   98818   4892     0  6012.0   \n",
       "日本本土        1  2021-05-27 14:07:43  731073     0  649955  12573     0     0.0   \n",
       "泰国          2  2021-05-27 08:27:36  137894     0   26873    873     0     0.0   \n",
       "\n",
       "      当日新增确诊  当日新增疑似  当日新增治愈  当日新增死亡  当日新增重症  当日现存确诊  当日境外输入   病死率  \n",
       "名称                                                                  \n",
       "突尼斯   1324.0     NaN   714.0    56.0     NaN   27600     NaN  0.04  \n",
       "塞尔维亚   387.0     NaN     0.0     0.0     NaN  704305     NaN  0.01  \n",
       "中国     655.0     NaN    13.0    11.0     NaN    5306    17.0  0.04  \n",
       "日本本土     2.0     NaN     0.0     1.0     NaN   68545     NaN  0.02  \n",
       "泰国    2455.0     NaN     0.0     0.0     NaN  110148     NaN  0.01  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "today_world.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fb74e70a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "编号                          0\n",
       "更新时间      2021-05-27 08:27:41\n",
       "累计确诊                   109016\n",
       "累计疑似                        0\n",
       "累计治愈                    98818\n",
       "累计死亡                     4892\n",
       "累计重症                        0\n",
       "累计境外输入                 6012.0\n",
       "当日新增确诊                  655.0\n",
       "当日新增疑似                    NaN\n",
       "当日新增治愈                   13.0\n",
       "当日新增死亡                   11.0\n",
       "当日新增重症                    NaN\n",
       "当日现存确诊                   5306\n",
       "当日境外输入                   17.0\n",
       "病死率                      0.04\n",
       "Name: 中国, dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "today_world.loc[\"中国\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "55b9c818",
   "metadata": {},
   "outputs": [],
   "source": [
    "world_top10 = today_world.sort_values(['累计确诊'],ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "71a50d27",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>55826.0</td>\n",
       "      <td>971.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5702760</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>印度</th>\n",
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       "      <td>3847.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>16274695</td>\n",
       "      <td>0</td>\n",
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       "      <td>454429</td>\n",
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       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>法国</th>\n",
       "      <td>10</td>\n",
       "      <td>2021-05-27 08:52:33</td>\n",
       "      <td>5683143</td>\n",
       "      <td>0</td>\n",
       "      <td>388128</td>\n",
       "      <td>109185</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12586.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1330.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5185830</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>土耳其</th>\n",
       "      <td>95046</td>\n",
       "      <td>2021-05-27 08:27:36</td>\n",
       "      <td>5212123</td>\n",
       "      <td>0</td>\n",
       "      <td>5057713</td>\n",
       "      <td>46787</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8738.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12205.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107623</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            编号                 更新时间      累计确诊  累计疑似      累计治愈    累计死亡  累计重症  \\\n",
       "名称                                                                            \n",
       "美国           7  2021-05-27 10:02:47  33971207     0  27662268  606179     0   \n",
       "印度          13  2021-05-27 14:07:44  27369093     0  24633951  315235     0   \n",
       "巴西   951234567  2021-05-27 08:27:36  16274695     0  14272174  454429     0   \n",
       "法国          10  2021-05-27 08:52:33   5683143     0    388128  109185     0   \n",
       "土耳其      95046  2021-05-27 08:27:36   5212123     0   5057713   46787     0   \n",
       "\n",
       "     累计境外输入    当日新增确诊  当日新增疑似    当日新增治愈  当日新增死亡  当日新增重症   当日现存确诊  当日境外输入   病死率  \n",
       "名称                                                                              \n",
       "美国      0.0   23500.0     NaN   55826.0   971.0     NaN  5702760     NaN  0.02  \n",
       "印度      0.0  211298.0     NaN  283135.0  3847.0     NaN  2419907     NaN  0.01  \n",
       "巴西      0.0   80486.0     NaN   40183.0  2398.0     NaN  1548092     NaN  0.03  \n",
       "法国      0.0   12586.0     NaN    1330.0   144.0     NaN  5185830     NaN  0.02  \n",
       "土耳其     0.0    8738.0     NaN   12205.0   166.0     NaN   107623     NaN  0.01  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_top10.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b551d9bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "world_top10 = world_top10[[\"累计确诊\",\"累计死亡\",\"病死率\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0e8d144d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 840x480 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 导入matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['font.sans-serif']=['SimHei']    #正常显示中文\n",
    "plt.rcParams['figure.dpi'] = 120      #设置所有图片的清晰度\n",
    "\n",
    "# 绘制条形图\n",
    "world_top10.sort_values('累计确诊').plot.barh(subplots=True,layout=(1,3),sharex=False,\n",
    "                                             figsize=(7,4),legend=False,sharey=True)\n",
    "\n",
    "plt.tight_layout()   #调整子图间距\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b6163ce4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>编号</th>\n",
       "      <th>名称</th>\n",
       "      <th>更新时间</th>\n",
       "      <th>累计确诊</th>\n",
       "      <th>累计疑似</th>\n",
       "      <th>累计治愈</th>\n",
       "      <th>累计死亡</th>\n",
       "      <th>累计重症</th>\n",
       "      <th>累计境外输入</th>\n",
       "      <th>累计境外输入确诊</th>\n",
       "      <th>累计境外输入死亡</th>\n",
       "      <th>累计境外输入治愈</th>\n",
       "      <th>当日新增确诊</th>\n",
       "      <th>当日新增疑似</th>\n",
       "      <th>当日新增治愈</th>\n",
       "      <th>当日新增死亡</th>\n",
       "      <th>当日新增重症</th>\n",
       "      <th>当日现存确诊</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>420000</td>\n",
       "      <td>湖北</td>\n",
       "      <td>2021-05-27 08:27:42</td>\n",
       "      <td>68159</td>\n",
       "      <td>0</td>\n",
       "      <td>63645</td>\n",
       "      <td>4512</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>810000</td>\n",
       "      <td>香港</td>\n",
       "      <td>2021-05-27 08:47:41</td>\n",
       "      <td>11836</td>\n",
       "      <td>0</td>\n",
       "      <td>11561</td>\n",
       "      <td>210</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>700000</td>\n",
       "      <td>台湾</td>\n",
       "      <td>2021-05-27 08:47:40</td>\n",
       "      <td>6091</td>\n",
       "      <td>0</td>\n",
       "      <td>1133</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>635</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>440000</td>\n",
       "      <td>广东</td>\n",
       "      <td>2021-05-27 08:57:48</td>\n",
       "      <td>2427</td>\n",
       "      <td>0</td>\n",
       "      <td>2371</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>310000</td>\n",
       "      <td>上海</td>\n",
       "      <td>2021-05-27 08:27:41</td>\n",
       "      <td>2079</td>\n",
       "      <td>0</td>\n",
       "      <td>2008</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       编号  名称                 更新时间   累计确诊  累计疑似   累计治愈  累计死亡  累计重症  累计境外输入  \\\n",
       "0  420000  湖北  2021-05-27 08:27:42  68159     0  63645  4512     0       0   \n",
       "1  810000  香港  2021-05-27 08:47:41  11836     0  11561   210     0       0   \n",
       "2  700000  台湾  2021-05-27 08:47:40   6091     0   1133    46     0       0   \n",
       "3  440000  广东  2021-05-27 08:57:48   2427     0   2371     8     0       0   \n",
       "4  310000  上海  2021-05-27 08:27:41   2079     0   2008     7     0       0   \n",
       "\n",
       "   累计境外输入确诊  累计境外输入死亡  累计境外输入治愈  当日新增确诊  当日新增疑似  当日新增治愈  当日新增死亡  当日新增重症  \\\n",
       "0       NaN       NaN       NaN       0     NaN       0       0     NaN   \n",
       "1       NaN       NaN       NaN       1     NaN       1       0     NaN   \n",
       "2       NaN       NaN       NaN     635     NaN       0      11     NaN   \n",
       "3       NaN       NaN       NaN      14     NaN       5       0     NaN   \n",
       "4       NaN       NaN       NaN       1     NaN       4       0     NaN   \n",
       "\n",
       "   当日现存确诊  \n",
       "0       0  \n",
       "1       0  \n",
       "2     624  \n",
       "3       9  \n",
       "4      -3  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "today_province = pd.read_csv(\"./today_province_2021_05_27.csv\")\n",
    "\n",
    "# 创建中文列名字典\n",
    "name_dict = {'date':'日期','name':'名称','id':'编号','lastUpdateTime':'更新时间',\n",
    "             'today_confirm':'当日新增确诊','today_suspect':'当日新增疑似',\n",
    "             'today_heal':'当日新增治愈','today_dead':'当日新增死亡',\n",
    "             'today_severe':'当日新增重症','today_storeConfirm':'当日现存确诊',\n",
    "             'total_confirm':'累计确诊','total_suspect':'累计疑似',\n",
    "             'total_heal':'累计治愈','total_dead':'累计死亡','total_severe':'累计重症',\n",
    "             'total_input':'累计境外输入','total_newConfirm':'累计境外输入确诊',\n",
    "             'total_newDead':'累计境外输入死亡','total_newHeal':'累计境外输入治愈'}\n",
    "\n",
    "# 更改列名\n",
    "today_province.rename(columns=name_dict,inplace=True)    # inplace参数是否在原对象基础上进行修改\n",
    "\n",
    "today_province.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "da95ac88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 34 entries, 0 to 33\n",
      "Data columns (total 18 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   编号        34 non-null     int64  \n",
      " 1   名称        34 non-null     object \n",
      " 2   更新时间      34 non-null     object \n",
      " 3   累计确诊      34 non-null     int64  \n",
      " 4   累计疑似      34 non-null     int64  \n",
      " 5   累计治愈      34 non-null     int64  \n",
      " 6   累计死亡      34 non-null     int64  \n",
      " 7   累计重症      34 non-null     int64  \n",
      " 8   累计境外输入    34 non-null     int64  \n",
      " 9   累计境外输入确诊  1 non-null      float64\n",
      " 10  累计境外输入死亡  1 non-null      float64\n",
      " 11  累计境外输入治愈  1 non-null      float64\n",
      " 12  当日新增确诊    34 non-null     int64  \n",
      " 13  当日新增疑似    0 non-null      float64\n",
      " 14  当日新增治愈    34 non-null     int64  \n",
      " 15  当日新增死亡    34 non-null     int64  \n",
      " 16  当日新增重症    0 non-null      float64\n",
      " 17  当日现存确诊    34 non-null     int64  \n",
      "dtypes: float64(5), int64(11), object(2)\n",
      "memory usage: 4.9+ KB\n"
     ]
    }
   ],
   "source": [
    "today_province.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "1657efd5",
   "metadata": {},
   "outputs": [
    {
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       "      <td>3206.352941</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2906.117647</td>\n",
       "      <td>143.882353</td>\n",
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       "      <td>1.886484</td>\n",
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       "      <td>106.992266</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>110000.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>25%</th>\n",
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       "      <td>294.250000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>283.500000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>671.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>75%</th>\n",
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       "      <td>1250.250000</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>NaN</td>\n",
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      "text/plain": [
       "                  编号          累计确诊  累计疑似          累计治愈         累计死亡  累计重症  \\\n",
       "count      34.000000     34.000000  34.0     34.000000    34.000000  34.0   \n",
       "mean   422941.176471   3206.352941   0.0   2906.117647   143.882353   0.0   \n",
       "std    195021.823359  11678.958139   0.0  10906.211918   772.670017   0.0   \n",
       "min    110000.000000      1.000000   0.0      1.000000     0.000000   0.0   \n",
       "25%    312500.000000    294.250000   0.0    283.500000     1.000000   0.0   \n",
       "50%    425000.000000    671.000000   0.0    653.500000     3.000000   0.0   \n",
       "75%    537500.000000   1250.250000   0.0   1111.250000     7.000000   0.0   \n",
       "max    820000.000000  68159.000000   0.0  63645.000000  4512.000000   0.0   \n",
       "\n",
       "       累计境外输入  累计境外输入确诊  累计境外输入死亡  累计境外输入治愈      当日新增确诊  当日新增疑似     当日新增治愈  \\\n",
       "count    34.0       1.0       1.0       1.0   34.000000     0.0  34.000000   \n",
       "mean      0.0     465.0       0.0     461.0   19.264706     NaN   0.382353   \n",
       "std       0.0       NaN       NaN       NaN  108.824103     NaN   1.101368   \n",
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       "50%       0.0     465.0       0.0     461.0    0.000000     NaN   0.000000   \n",
       "75%       0.0     465.0       0.0     461.0    0.000000     NaN   0.000000   \n",
       "max       0.0     465.0       0.0     461.0  635.000000     NaN   5.000000   \n",
       "\n",
       "          当日新增死亡  当日新增重症      当日现存确诊  \n",
       "count  34.000000     0.0   34.000000  \n",
       "mean    0.323529     NaN   18.558824  \n",
       "std     1.886484     NaN  106.992266  \n",
       "min     0.000000     NaN   -3.000000  \n",
       "25%     0.000000     NaN    0.000000  \n",
       "50%     0.000000     NaN    0.000000  \n",
       "75%     0.000000     NaN    0.000000  \n",
       "max    11.000000     NaN  624.000000  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "today_province.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "101fe723",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将各省名称设置为索引\n",
    "today_province.set_index('名称',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "bfe6fb32",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>635</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广东</th>\n",
       "      <td>440000</td>\n",
       "      <td>2021-05-27 08:57:48</td>\n",
       "      <td>2427</td>\n",
       "      <td>0</td>\n",
       "      <td>2371</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>上海</th>\n",
       "      <td>310000</td>\n",
       "      <td>2021-05-27 08:27:41</td>\n",
       "      <td>2079</td>\n",
       "      <td>0</td>\n",
       "      <td>2008</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        编号                 更新时间   累计确诊  累计疑似   累计治愈  累计死亡  累计重症  累计境外输入  \\\n",
       "名称                                                                        \n",
       "湖北  420000  2021-05-27 08:27:42  68159     0  63645  4512     0       0   \n",
       "香港  810000  2021-05-27 08:47:41  11836     0  11561   210     0       0   \n",
       "台湾  700000  2021-05-27 08:47:40   6091     0   1133    46     0       0   \n",
       "广东  440000  2021-05-27 08:57:48   2427     0   2371     8     0       0   \n",
       "上海  310000  2021-05-27 08:27:41   2079     0   2008     7     0       0   \n",
       "\n",
       "    累计境外输入确诊  累计境外输入死亡  累计境外输入治愈  当日新增确诊  当日新增疑似  当日新增治愈  当日新增死亡  当日新增重症  \\\n",
       "名称                                                                         \n",
       "湖北       NaN       NaN       NaN       0     NaN       0       0     NaN   \n",
       "香港       NaN       NaN       NaN       1     NaN       1       0     NaN   \n",
       "台湾       NaN       NaN       NaN     635     NaN       0      11     NaN   \n",
       "广东       NaN       NaN       NaN      14     NaN       5       0     NaN   \n",
       "上海       NaN       NaN       NaN       1     NaN       4       0     NaN   \n",
       "\n",
       "    当日现存确诊  \n",
       "名称          \n",
       "湖北       0  \n",
       "香港       0  \n",
       "台湾     624  \n",
       "广东       9  \n",
       "上海      -3  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "today_province.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b398be66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "编号                       340000\n",
       "更新时间        2021-05-27 08:27:41\n",
       "累计确诊                       1004\n",
       "累计疑似                          0\n",
       "累计治愈                        990\n",
       "累计死亡                          6\n",
       "累计重症                          0\n",
       "累计境外输入                        0\n",
       "累计境外输入确诊                    NaN\n",
       "累计境外输入死亡                    NaN\n",
       "累计境外输入治愈                    NaN\n",
       "当日新增确诊                        0\n",
       "当日新增疑似                      NaN\n",
       "当日新增治愈                        1\n",
       "当日新增死亡                        0\n",
       "当日新增重症                      NaN\n",
       "当日现存确诊                       -1\n",
       "Name: 安徽, dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "today_province.loc[\"安徽\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "45e54028",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "名称\n",
       "台湾     635\n",
       "广东      14\n",
       "福建       1\n",
       "上海       1\n",
       "香港       1\n",
       "内蒙古      1\n",
       "北京       1\n",
       "四川       1\n",
       "山西       0\n",
       "天津       0\n",
       "Name: 当日新增确诊, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看全国新增确诊top10的地区\n",
    "new_top10 = today_province['当日新增确诊'].sort_values(ascending=False)[:10]\n",
    "\n",
    "new_top10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e4350390",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制条形图和饼图\n",
    "fig,ax = plt.subplots(1,2,figsize=(10,5))\n",
    "\n",
    "new_top10.sort_values(ascending=True).plot.barh(fontsize=10,ax=ax[0])\n",
    "new_top10.plot.pie(autopct='%.1f%%',fontsize=10,ax=ax[1])\n",
    "\n",
    "plt.ylabel('')\n",
    "plt.title('全国新增确诊top10地区',size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a531b2c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看全国现存确诊人数top10的省市\n",
    "store_top10 = today_province['当日现存确诊'].sort_values(ascending=False)[:10]\n",
    "\n",
    "# 绘制条形图\n",
    "\n",
    "store_top10.sort_values(ascending=True).plot.barh(fontsize=10)\n",
    "\n",
    "plt.title('全国现存确诊top10地区',size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "cdf2509e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日期</th>\n",
       "      <th>更新时间</th>\n",
       "      <th>累计确诊</th>\n",
       "      <th>累计疑似</th>\n",
       "      <th>累计治愈</th>\n",
       "      <th>累计死亡</th>\n",
       "      <th>累计重症</th>\n",
       "      <th>累计境外输入</th>\n",
       "      <th>累计境外输入确诊</th>\n",
       "      <th>当日新增确诊</th>\n",
       "      <th>当日新增疑似</th>\n",
       "      <th>当日新增治愈</th>\n",
       "      <th>当日新增死亡</th>\n",
       "      <th>当日新增重症</th>\n",
       "      <th>当日现存确诊</th>\n",
       "      <th>当日现存境外输入</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-03-28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102698</td>\n",
       "      <td>0</td>\n",
       "      <td>97457</td>\n",
       "      <td>4851</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5277</td>\n",
       "      <td>390</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-03-29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102715</td>\n",
       "      <td>0</td>\n",
       "      <td>97483</td>\n",
       "      <td>4851</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5285</td>\n",
       "      <td>381</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-03-30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102734</td>\n",
       "      <td>0</td>\n",
       "      <td>97499</td>\n",
       "      <td>4851</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5290</td>\n",
       "      <td>384</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-03-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102762</td>\n",
       "      <td>0</td>\n",
       "      <td>97518</td>\n",
       "      <td>4851</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5300</td>\n",
       "      <td>393</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102790</td>\n",
       "      <td>0</td>\n",
       "      <td>97541</td>\n",
       "      <td>4851</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5305</td>\n",
       "      <td>398</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           日期  更新时间    累计确诊  累计疑似   累计治愈  累计死亡  累计重症  累计境外输入  累计境外输入确诊  \\\n",
       "0  2021-03-28   NaN  102698     0  97457  4851   NaN    5277       390   \n",
       "1  2021-03-29   NaN  102715     0  97483  4851   NaN    5285       381   \n",
       "2  2021-03-30   NaN  102734     0  97499  4851   NaN    5290       384   \n",
       "3  2021-03-31   NaN  102762     0  97518  4851   NaN    5300       393   \n",
       "4  2021-04-01   NaN  102790     0  97541  4851   NaN    5305       398   \n",
       "\n",
       "   当日新增确诊  当日新增疑似  当日新增治愈  当日新增死亡  当日新增重症  当日现存确诊  当日现存境外输入  \n",
       "0      18       0       0       0     NaN       0        15  \n",
       "1      17       0       0       0     NaN       0         8  \n",
       "2      19       1       0       0     NaN       0         5  \n",
       "3      28       0       0       0     NaN       0        10  \n",
       "4      28       2       0       0     NaN       0         5  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "alltime_china = pd.read_csv(\"./alltime_China_2021_05_27.csv\")\n",
    "\n",
    "# 创建中文列名字典\n",
    "name_dict = {'date':'日期','name':'名称','id':'编号','lastUpdateTime':'更新时间',\n",
    "             'today_confirm':'当日新增确诊','today_suspect':'当日新增疑似',\n",
    "             'today_heal':'当日新增治愈','today_dead':'当日新增死亡',\n",
    "             'today_severe':'当日新增重症','today_storeConfirm':'当日现存确诊',\n",
    "             'today_input':'当日现存境外输入',\n",
    "             'total_input':'累计境外输入','total_storeConfirm':'累计境外输入确诊',\n",
    "             'total_confirm':'累计确诊','total_suspect':'累计疑似',\n",
    "             'total_heal':'累计治愈','total_dead':'累计死亡','total_severe':'累计重症'}\n",
    "\n",
    "# 更改列名\n",
    "alltime_china.rename(columns=name_dict,inplace=True)\n",
    "\n",
    "alltime_china.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "efe2a0f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 60 entries, 0 to 59\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   日期        60 non-null     object \n",
      " 1   累计确诊      60 non-null     int64  \n",
      " 2   累计疑似      60 non-null     int64  \n",
      " 3   累计治愈      60 non-null     int64  \n",
      " 4   累计死亡      60 non-null     int64  \n",
      " 5   累计重症      0 non-null      float64\n",
      " 6   累计境外输入    60 non-null     int64  \n",
      " 7   累计境外输入确诊  60 non-null     int64  \n",
      " 8   当日新增确诊    60 non-null     int64  \n",
      " 9   当日新增疑似    60 non-null     int64  \n",
      " 10  当日新增治愈    60 non-null     int64  \n",
      " 11  当日新增死亡    60 non-null     int64  \n",
      " 12  当日现存确诊    60 non-null     int64  \n",
      " 13  当日现存境外输入  60 non-null     int64  \n",
      "dtypes: float64(1), int64(12), object(1)\n",
      "memory usage: 6.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# 缺失值处理\n",
    "\n",
    "# 计算当日现存确诊人数\n",
    "alltime_china['当日现存确诊'] = alltime_china['累计确诊']-alltime_china['累计治愈']-alltime_china['累计死亡']\n",
    "\n",
    "# 删除更新时间一列\n",
    "alltime_china.drop(['更新时间','当日新增重症'],axis=1,inplace=True)\n",
    "\n",
    "alltime_china.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a53f8a99",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将日期改成datetime格式\n",
    "alltime_china['日期'] = pd.to_datetime(alltime_china['日期'])\n",
    "\n",
    "# 设置日期为索引\n",
    "alltime_china.set_index('日期',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "3f29b6e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "累计确诊        103371.0\n",
       "累计疑似             0.0\n",
       "累计治愈         97973.0\n",
       "累计死亡          4856.0\n",
       "累计重症             NaN\n",
       "累计境外输入        5543.0\n",
       "累计境外输入确诊       542.0\n",
       "当日新增确诊          31.0\n",
       "当日新增疑似           0.0\n",
       "当日新增治愈           0.0\n",
       "当日新增死亡           0.0\n",
       "当日现存确诊         542.0\n",
       "当日现存境外输入        19.0\n",
       "Name: 2021-04-20 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alltime_china.loc['2021-04-20']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e7bedda2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import dates\n",
    "# 时间序列数据绘制折线图\n",
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "\n",
    "alltime_china['当日新增确诊'].plot(ax=ax, style='-',lw=1,color='c',marker='o',ms=3)\n",
    "\n",
    "ax.xaxis.set_major_locator(dates.MonthLocator())    #设置间距\n",
    "ax.xaxis.set_major_formatter(dates.DateFormatter('%b'))    #设置日期格式\n",
    "\n",
    "fig.autofmt_xdate()    #自动调整日期倾斜\n",
    "\n",
    "plt.title('全国新冠肺炎新增确诊病例折线图',size=15)\n",
    "plt.ylabel('人数')\n",
    "plt.grid(axis='y')\n",
    "plt.box(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0cb658e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
